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The American College of Cardiology (ACC) and the American Heart Association (AHA) recommend an electrocardiogram (ECG) be obtained and interpreted within 10-minutes of arrival to improve patient outcomes (Takakuwa, Burek, Estepa, & Shofer, 2009).  The American College of Cardiology (ACC) and the American Heart Association (AHA)recommends when a ST-elevation myocardial infraction (STEMI) is identified that a percutaneous coronary intervention (PCI) be performed within 90 minutes or less (Takakuwa et al., 2009).  To date there is no standard process for emergency departments (EDs) to follow to obtain an ECG within 10-minutes of arrival (Yaa et al., 2017).  The evidence supporting aggressive door to reperfusion is associated with lower mortality is sizeable, and one-way to lower the door-to-reperfusion time is a timely ECG(Chandra et al., 2009).  CINAHL, MEDLINE, and PUBMED CENTRAL databases were searched for project evidence.  The terms emergency department, electrocardiogram, American Heart Association, and American College of Cardiology were used in all databases.  The Boolean operator AND was used to limit search results.  The PICOT question for the project is:  In patients that present to the emergency department (ED) with a complaint of chest pain, how does obtaining an electrocardiogram affect the early identification for the need of percutaneous coronary intervention (PCI).  The hospital identified for the project is a small intercity community hospital that does not have the ability to perform PCI on site and requires all STEMI patients to be transferred out of the facility

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The intended outcome for the poster is to serve as a resource, both for myself and for my fellow classmates. By placing definitions and examples on an open site both myself and classmates will be able to utilize this padlet as a resource as it will have well defined examples and definitions.

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Before and after interventional study - Non-experimental design commonly used in safety studies.  This form should be used with caution as there is threat with internal validity (refers to how well an experiment is carried out, the extent to which a causal conclusion based on a study is warranted, which is determined by the degree to which a study minimizes error/bias – only relevant in studies that try to establish a causal relationship.  Not relevant in most observational or descriptive studies) Campbell, M., Machin, D., & Walters, S. (2007) & Web Center for Social Research Methods

Descriptive statistics should be used for this study.

Mean, standard deviation, confidence interval - interquartile ranges (IQRs)

chi-square or Fisher's exact test

Wilcoxon rank sum test

Allows for comparison of before and after

Prospective Cohort Study - · One or more samples are followed prospectively and subsequent status evaluations with respect to a disease or outcome are conducted to determine which initial participants exposure are associated with it.

A research study that compare a particular outcome in groups of individuals who are alike in many ways but differ by a certain characteristic

Univariate analysis - the simplest form of analyzing data. “Uni” means “one”, so in other words your data has only one variable. It doesn't deal with causes or relationships (unlike regression) and it's major purpose is to describe; it takes data, summarizes that data and finds patterns in the data

Mann-Whitney U test

Kruskal-Wallis

Retrospective cohort study - A research study in which medical records of groups of individuals who are alike in many ways but differ by a certain characteristic are compared for a particular outcome

Logistic regression

Quantile regression

Bootstrap resampling was used to estimate standard errors and confidence intervals

Kruskal-Wallis test is used for comparing ordinal or non-Normal variables for more than two groups, and is a generalization of the Mann-Whitney U test

Analysis of variance  is used to compare normally distributed variables for more than two groups = Kruskal-Wallis test

T-test

Longitudinal quantitive study - · Investigation in which data are collected from a number of people over a long period of time· Longitudinal studies involve multiple follow-up measurements.

Purpose is to gather and analyze data over time.

Univariate (ANOVA) or multivariate (MANOVA) analysis of variance – either case equal interval lengths and normal distribution in all groups and only means are compared – this sacrifices individual data

Mixed regression model (MRM) – focuses on individual change over time, accounting for variation in the timing of repeated measures, and for missing or unequal data instances

Generalized estimating equation (GEE) rely on independence of individuals within the population to focus primarily on the regression data

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central tendency - is a single value that describes the way in which a group of data cluster around a central value.

a way to describe the center of a data set.

three measures of central tendency: the mean, the median, and the mode.

Convenience sample - main types of non-probability sampling methods.

A convenience sample is made up of people who are easy to reach.

(also known as grab sampling, accidental sampling, or opportunity sampling)

type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand. This type of sampling is most useful for pilot testing

Cronbach alpha -  is a test of internal consistency and frequently used to calculate the correlation values among the answers on your assessment tool (Sullivan, 2011)

A high reliability estimate should be as close to 1 as possible (Sullivan, 2011).

Dependent variable - the event studied and expected to change when the independent variableis changed

Independent variable - variable that is intentionally changed to observe its effect on the dependent variable.

Generalization - the extension of research findings and conclusions from a study conducted on a sample population to the population at large.

Kruskal-Wallis Test- nonparametric distribution free test, and is used when the assumptions of one-way ANOVA are not met.

Both the Kruskal-Wallis test and one-way ANOVA assess for significant differences on a continuous dependent variable by a categorical independent variable (with two or more groups).

In the ANOVA, we assume that the dependent variable is normally distributed and there is approximately equal variance on the scores across groups.

The Kruskal-Wallis test can be used for both continuous and ordinal-level dependent variables.

However, like most non-parametric tests, the Kruskal-Wallis Test is not as powerful as the ANOVA

Level of measurement - A variable has one of four different levels of measurement: Nominal, Ordinal, Interval, or Ratio.  (Interval and Ratio levels of measurement are sometimes called Continuous or Scale).  It is important for the researcher to understand the different levels of measurement, as these levels of measurement, together with how the research question is phrased, dictate what statistical analysis is appropriate.

Nominal–Latin for name only (Republican, Democrat, Green, Libertarian)

first level of measurement - numbers in the variable are used only to classify the data.  In this level of measurement, words, letters, and alpha-numeric symbols can be used.

I.E. data about people belonging to three different gender categories. In this case, the person belonging to the female gender could be classified as F, the person belonging to the male gender could be classified as M, and transgendered classified as T  = nominal level of measurement.

Ordinal–Think ordered levels or ranks (small–8oz, medium–12oz, large–32oz)

second level of measurementis the ordinal level of measurement- some ordered relationship among the variable’s observations

student scores the highest grade of 100 in the class = first rank.

The next highest score is 92 = second rank.

The ordinal level of measurement indicates an ordering of the measurements.

Interval–Equal intervals among levels (1 dollar to 2 dollars is the same interval as 88 dollars to 89 dollars)

third level of measurementis the interval level of measurement

classifies and orders the measurements

specifies that the distances between each interval on the scale are equivalent along the scale from low interval to high interval.

For example, an interval level of measurement could be the measurement of anxiety in a student between the score of 10 and 11, this interval is the same as that of a student

Ratio – Let the “o” in ratio remind you of a zero in the scale (Day 0, day 1, day 2, day 3, …)

fourth level of measurementi

in addition to having equal intervals,

can have a value of zero as well

The zero in the scale makes this type of measurement unlike the other types of measurement, although the properties are similar to that of the interval level of measurement.

In the ratio level of measurement, the divisions between the points on the scale have an equivalent distance between them

Mann Whitney U test-is the non-parametric alternative test to theindependent sample t-test

non-parametric test that is used to compare two sample means that come from the same population

used to test whether two sample means are equal or not.

used when the data is ordinal or when the assumptions of the t-test are not met

Measurement sensitivity -  the sensitivity of the measure to detect change (i.e. does the measure have a scale that assesses a large range of response?) and bidirectional change (i.e. can change in two directions be detected?) (Kazdin, 2003)

Parametric statistics - makes an assumption about the population parameters and the distribution the data came from

Power analysis - an important aspect of experimental design. It allows us to determine the sample size required to detect an effect of a given size with a given degree of confidence. ... power = 1 - P(Type II error) = probability of finding an effect that is there

Psychometric characteristics - the reliability (how consistent is the measure and validity (does the measure and its content assess the construct?) (Kazdin, 2003)

Statistically significant AND clinically important - This is where there is an important, meaningful difference between the groups and the statistics also support this. (difference is neither clinically nor statistically significant).

Not statistically significant BUT clinically important - This is most likely to occur if your study is underpowered and you do not have a large enough sample size to detect a difference between groups. In this case you might fail to detect an important difference between groups.

Statistically significant BUT NOT clinically important - This is more likely to happen the larger sample size you have. If you have enough participants, even the smallest, trivial differences between groups can become statistically significant. It’s important to remember that, just because a treatment is statistically significantly better than an alternative treatment, does not necessarily mean that these differences are clinically important or meaningful to patient

Reliability – internal validity: repeating a piece of research in order to establish the reliability of the findings – meaning consistency and dependency of a measure – repeatability – test-retest reliability

refers to whether an assessment instrument gives the same results each time it is used in the same setting with the same type of subjects.

essentially means consistent or dependable results.

part of the assessment of validity(Sullivan, 2011)

Type I Error – falsely infers the existence of something that is not there· Type II falsely infers the absence of something that is not there

Type II Error -falsely infers the absence of something that is
****Poor design, poor sample size planning leads to type II errors****

Validity - in research refers to how accurately a study answers the study question or the strength of the study conclusions (Sullivan, 2011).

For outcome measures such as surveys or tests, validity refers to the accuracy of measurement (Sullivan, 2011).

how well the assessment tool actually measures the underlying outcome of interest (Sullivan, 2011).

the interpretation or specific purpose of the assessment tool with particular settings and learners (Sullivan, 2011).

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Critical Appraisal Tools
https://www.cebm.net/2014/06/critical-appraisal/

Critical Appraisal Checklist
https://casp-uk.net/casp-tools-checklists/

https://casp-uk.net/wp-content/uploads/2018/03/CASP-Cohort-Study-Checklist

Levels of Evidence
https://www.youtube.com/watch?v=5H8w68sr0u8

Joanna Briggs
https://www.youtube.com/watch?v=JpECytOTEF4

http://joannabriggs.org/research/critical-appraisal-tools.html

IOWA Model:
https://www.youtube.com/watch?v=fpIN-o0Gapo

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Takakuwa, K. M., Burek, G. A., Estepa, A. T., & Shofer, F. S. (2009). A method for improving arrival-to-electrocardiogram time in emergency department chest pain patients and the effect on door-to-balloon time for ST-segment elevation myocardial infarction. Academic Emergency Medicine, 16(10), 921-27. doi: 10.1111/j.1553-2712.2009.00493.x

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Takakuwa, K. M., Burek, G. A., Estepa, A. T., & Shofer, F. S. (2009). A method for improving arrival-to-electrocardiogram time in emergency department chest pain patients and the effect on door-to-balloon time for ST-segment elevation myocardial infarction. Academic Emergency Medicine, 16(10), 921-27. doi: 10.1111/j.1553-2712.2009.00493.x

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Goal of Article:

Determine if an emergency department could improve adherence to a door-to-ECG time goal of 10 mins or less for patients who presented to an ED with chest pain and effect of this adherence on door-to-balloon time for ST-segment elevation myocardial infarction

Null hypothesis we could not improve the percentage of patients who received an ECG within 10 mins

IRB approved

Method:

One month planned before-and-after interventional study design for implementing a new process for obtaining ECGs in patients presenting with chest pain.

Before intervention: patients would enter the ED, directed to registration desk for a brief registration, then sent to triage. Triage nurse would then assess the patient and order an ECG if ACS was suspected.  ECG would be taken by any available technician and could be delayed if the technician decided to insert an IV, draw blood, and deliver the blood to the nurse assigned to the patient.

After intervention: No increased staffing needed. All nonclinical staff in ED waiting room would direct patients to registration desk. The registration clerks
were trained to ask the patient for their chief complaint. If the chief complaint was chest pain and not trauma-related, a quick registration was completed. The registration clerk would overhead page or directly call the ECG technicinca to the triage station.  At the start of every shift one ED tech. would be assigned to call the new ECG cell phone.  There was one back-up ECG tech was identified.

Sample:

The sample is heterogeneous with respect to race and sex.

318 before

405 after

Mean age 50 ±16 yrs

54% women

57% African American

36% white

7% other races

89% arrived on their own

39% triaged as emergent

61% triaged as nonemergent

68% presented during day time hours

32%  presented at night time

All patients aged 18 years and older, who presented to ED between 9/01/07-10/31/07 with the mention of chest pain would be included

Data:

Patients were dichotomized to as emergent - ESI 1-2 or non emergent - ESI 3-4

ECG times were reported as medians +/- interquartile ranges - dichotomized as </= 10 mins. or > 10 mins.

Mode of arrival and time of arrival analyzed using chi-square or Fisher's exact

Differences in the proportions of patients receiving ECG in </= 10 mins before and after intervention were calculate with 95% confidence intervals (CIs)

Adjusting for possible cofounders relative risk regression using Gaussian estimating equation was performed

Post hoc analysis to examine door-to-ECG time and DTB time for STEMI alert patient was performed using the Wilcoxon ranking sum test

SAS statistical software Version 9.1

A probability <0.05 considered statistically significant

Results

Before intervention: 16% of chest pain (CP) pts. Received ECGs at 10 mins or less and the median. was 16 mins = IQR 12-24

After intervention: 64% received ECG within 10 mins and the median time was 9 mins IQR = 8-12 mins

Difference between the groups was 47.3% (95% CI=40.8% to 53.3%, p<0.0001

Largest improvement was for walk-in patients: 67% had an ECG within 10 mins compared to the 15% before intervention (difference 52%, 95%CI = 46% to 58%)

Recommendations:

Include all patient with ACS symptoms for rapid ECGs

Limitations of Study:

Limited to a single hospital

Urban academic environment

Relatively short duration - improvements seen may lessens over time

Only included patients with a c/o chest pain

Do not account for ED attending interpretation time

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Pearlman, M. K., Tanabe, P., Mycyk, M. B., Zull, D. N., & Stone, D. B. (2008). Evaluating disparities in door-to-EKG time for patient with noncardiac chest pain. Journal of Emergency Nursing, 34(5), 414-18. doi: 10.1016/j.jen.2007.07.002

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Pearlman, M. K., Tanabe, P., Mycyk, M. B., Zull, D. N., & Stone, D. B. (2008). Evaluating disparities in door-to-EKG time for patient with noncardiac chest pain. Journal of Emergency Nursing, 34(5), 414-18. doi: 10.1016/j.jen.2007.07.002

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Goal of Article:

Examine gender, racial, and age differences in door-to-ECG time in patients diagnosed with non-cardiac chest pain

Method:

prospective cohort study of adult emergency department patients who were admitted to an Emergency Department Observation Unit (EDOU) to complete evaluation of chest pain

Gained IRB approval

Sample:

The sample is heterogeneous with respect to age and sex.

A convenience sample was used

September through December 2003

Included all patients regardless of the mode of arrival to the ED with a chief complaint of chest pain at low-risk for STEMI/ACS

Males :n=107

Females n=107

Mean age male: 44

Mean age female: 41

There were more white men than white woman

There were more black women than black men

There were more Asian women than men

Sex/racial – woman older than men (t=-2.4, p=0.01, mean difference 3.86, 95% CI -6.9, -0.77

racial there are more white men than woman, more black women than black men, and more Asian women than men (Pearson X2=27.2 p=0.00). No statically difference between sex and race

Only patients that were admitted to the observation unit were included

Only patients considered low risk for STEMI/ACS
Only English speaking patients were used – leaving out a potential entire subset

Only one hospital was studied – however the annual census is 70,000

Only English speaking patients were used – leaving out a potential entire subset

Data:

Data entered into Microsoft Access database

Analyzed by SPSS version 14

Univariate analysis was used using nonparametric statistics with the Mann Whitney U test or Kruskal-Wallis test (due to the positive skew of the time-to-ECG)

Actual difference in minutes to ECG between groups for each variable presented as medians with intra-quartile range.  The level of significance was set at 0.05

The study outcome variable was time to initial ECG - determined by subtracting the time of arrival as documented by triage nurse from the time printed on the ECG

Independent variables are:  self-reported age, race, and sex

Age was categorized into three groups: 18-39, 40-59, & older than 65

Actual differences in minutes to EKG between groups for each variable are presented as medians with the intra-quartile range

Results:

250 patients met eligibility requirements

6 patients met exclusion criteria

29 declined to participate

n=214

The median time to EKG was 29 minutes – intra-quartile range = 19-52 minutes, range 1-495

The EKG times was scientifically greater for patients in the age categories 18-39 and 40-59

Sex/racial – woman older than men (t=-2.4, p=0.01, mean difference 3.86, 95% CI -6.9, -0.77)

racial there are more white men than woman, more black women than black men, and more Asian women than men (Pearson X2=27.2 p=0.00)

No statically difference between sex and race

The Pearson X²= 27.1, P=0.00 showing no sex and racial disparities as shown with a p value of less then 0.05

Recommendations:

Future research to understand the predictions of prolonged time to ECG may help inform local quality improvement efforts

Limitations of Study:

single site used

results may not be generalizable

Convenience sampling was used to enroll patients between noon and 8 pm

Only patients considered to be low risk for STEMI/ACS were enrolled

Small number of Asians and Hispanic patients

Possible that inaccurate times were recorded by triage nurse

Power analysis was not performed to determine the necessary sample size

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Atzema, C. L., Austin, P. C., Tu, J. V., & Schull, M. J. (2011). Effect of time to electrocardiogram on time from electrocardiogram to fibrinolysis in acute myocardial infarction patients. Canadian Association of Emergency Physicians, 13(2), 79-89. doi: 10.2310/8000.2011.110261

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Atzema, C. L., Austin, P. C., Tu, J. V., & Schull, M. J. (2011). Effect of time to electrocardiogram on time from electrocardiogram to fibrinolysis in acute myocardial infarction patients. Canadian Association of Emergency Physicians, 13(2), 79-89. doi: 10.2310/8000.2011.110261

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Goal of Article:

Sought to establish an evidence based benchmark door-to-ECG time

Method:

retrospective cohort study

Sample:

2,961 STEMI patients

Mean age was 62.7 (SD ±13.1 yrs) and 70.9% were male

Data:

Logistic regression was used to determine relationship between door-to-ECG time and the probability of meeting the AHA benchmark door-to-needle time – this analysis was ONLY used on patients with a door-to-ECG time up to 29 minutes due to the fact that it would be impossible to make the benchmark time of 30 minutes

Quantile regression was used to meet the secondary objective of the independent effect of door-to-ECG time on ECG-to-needle time

Quantile regression models effect the predictor variables on the median of the dependent variable instead of the mean

Quantile regression suited to distributions that are susceptible to being skewed

Bootstrap resampling was used to estimate standard errors and confidence intervals

The Kruskal-Wallis test is used for comparing ordinal or non-Normal variables for more than two groups, and is a generalization of the Mann-Whitney U test.

Analysis of variance is used to compare normally distributed variables for more than two groups = Kruskal-Wallis test

If the outcome variable is the dependent variable, and the residuals are plausible then the distribution of the independent variable is not important.

Plot data

Pearson

Analysis of Variance

Linear regression

Logic regression

T-test

Cubic smoothing Splines – relationship between door-to-ECG and ECG-to-needle and the proportion of STEMI patients who met the benchmark door-to-needle time of 30 mins based on their door-to-ECG time

Results

Median door-to-ECG time was 8 mins (IQR 4.0-15.0 mins)

Median door-to-needle time was 39.0 min (IQR 25.0-66.0 mins)

Median ECG-to-needle time was 27.0 mins (IQR 16.0-50.0 mins)

30 day mortality rate was 8.0% (95% CI 7.1-9.0)

a gradual increase in ECG-to-needle time as the door-to-ECG time increases from 0 to 15 to 30 mins, the ECG-to-needle time increase from 38 to 47 to 52.5 mins.

Recommendations:

Future research on triage ECGs is needed

Limitations of Study:

age of the data

Only one site was completing pre-hospital ECGs (which were removed from analysis)

ED clocks were not synchronized

Unable to account for ED triage score

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Ballard, N., Bairan, A. Newberry, L, Van Brackie, L., & Barnett, G.  (2011).  Effect of education on a chest pain mnemonic on door-to-ecg time.  Journal of Emergency Nursing 37(3), 220-224.  doi: 10.1016/j.jen.2010.02.018

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Ballard, N., Bairan, A. Newberry, L, Van Brackie, L., & Barnett, G.  (2011).  Effect of education on a chest pain mnemonic on door-to-ecg time.  Journal of Emergency Nursing 37(3), 220-224.  doi: 10.1016/j.jen.2010.02.018

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Goal of Article:

Evaluate the effect of a new chest pain mnemonic (CPM) as a teaching tool for rapid recognition of AMI patients arriving by self-transport in ED triage in an effort to improve door-to-ECG

Method:

longitudinal, quasi-experimental quantitative study

pre-and-post test

Sample:

26 nurses

4 emergency departments

Ad Hoc queries of the National Registry for Myocardial Infarction databases for patients arriving by self-transport

Data:

Mean pre-test score = 81.5

Mean post-test score = 84.6

The log-linear model is used for this study – extension of the x2 analysis – similar to regression model

The odds of receiving an ECG within 10 mins of arrival are modeled as a function of the variables – intervention, gender, and hospital

Additional analysis was performed to determine whether gender had any effects on the percentage of patient receiving ECGS within 10 mins

Differences in pretest and post-test scores for education were analyzed by use of a paired ttest

Data was analyzed by use of a paired t test, the results were not statistically significant

Results:

No significant difference in the overall population for pre- and post-intervention percentages of patients receiving ECGs within 10 mins

The pre- and post-intervention percentages of patients meeting the 10 minute goal differed significantly for hospital 1 (X²= 10.59, p= 0.0011)

The post intervention difference increased in hospital 4 but was not significant (X²= 3.44, p= 0.0637) – because of the larger estimated standard error resulting from the smaller sample size in hospital 4 – This explains why the intervention main effect is not significantly different in the aggregate

Recommendations:

investigate intra-hospital differences in chest pain triage

review and revise the CPM and the education plan with more attention to gender specific differences that should trigger an initial ECG

Repeat the study with a research asssitanct at each facility to identify extraneous variables that could impact DTE time

Include a mechanism which participants. can identify any barriers encountered in achieving the DTE time of 10 mins in AMI patients

Limitations of Study:

inability to examine extranous variables that may have contributed to changes in DTE

Loss of study investigator before final post-test and data analysis

Low enrollment of ER nurses

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Maier, G., & Martins, E. A. (2016). Health care for patient with acute coronary syndrome according to quality indicators. Rev Bras Enferm, 69(4), 757-764. doi: 10.1590/0034-7167.2016690420i

# data[].attributes.subject
Maier, G., & Martins, E. A. (2016). Health care for patient with acute coronary syndrome according to quality indicators. Rev Bras Enferm, 69(4), 757-764. doi: 10.1590/0034-7167.2016690420i

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Goal of Article:

assess in-hospital care for patients with ACS according to quality indicators

Method:

longitudinal, descriptive-expiatory study with a quantitative approach

Sample:

A convenience sample was used

94 patients

Mean age of 54 years

52.1% male

55.3% were white

59.5% unemployed

Non-probabilistic

11/12 - 3/13

Inclusion criteria: to be aged more than 18, agree to participate bu signing an informed consent, and confirm the diagnosis of ACS through medical records.

Data:

Data collect in two stages:

One:  upon admission of patients to the ED, a form was applied through an interview, including questions about the socio-demographic characteristics and history

Two: variables r/t length

of time of treatment, exams performed,  administered, and in-hopsital outcomes were collected from medical records

Data stored using SPSS 20.0

Categorical variables presented as absolute and relative frequencies and numerical variables as mean. Median, maximum, minimum, and standard deviation

Chi-square test used for categorical variables

Numerical variable as mean, median, maximum, minimum and standard deviation

Numerical values that showed a normal distribution were analyzed by a student-t test (door-to-ECG time and door-to-balloon time

Non-parametric distribution were analyzed by the Mann-Whitney test – length of hospitalization and length of stay in the ED

Data is summarized precisely and depicts the sample.

Results:

Median was used to report out the following:

Door-to-ECG = 40

Length of hospitalization in days = 6.0

Length of stay in the ED in days = 4.0

A power analysis was not used to determine the sample size.

The power of the study is positive for the sample size, however, it is not generalizable to the general population due to the fact that they study is carried out one facility.

TIMI score pvalue = 0.033* – any value less than 0.05 is statistically significant

GRACE score pvalue = 0.001

* both above pvalues were calculated using the chi-square test as the data was categorical

The door-to-ECG time was calculated using the Student t test.

The pvalue = 0.37
This door-to-ECG time is what I need to use for my DNP project so this is the main number I focused on in the study originally.  The pvalue of 0.37 indicated that there was a range of ECG times with no consistency of the recommended door-to-ECG time of 10 or less. – Only 7.4% of patients received an ECG within the first 10 mins

Both door-to-ECG time and door-to-balloon time were higher than recommended and only 7.4% of patients had an ECG within 10 mins of arrival

Recommendations:

Protocola are required to standardized practice and improve the indicators within the study

Limitations of Study:

time set for data collection

lack of information

Records of procedures performed and given to patients admitted due to external transfers

Adoption of the medical record

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After completing the tables required for this class I will change my approach to reading and evaluating quantitative articles. The plan for evaluation is to use a systematic approach. I will use one of the critical appraisal tools to begin the evaluation. I will read through the articles and highlight as I read. I feel better prepared as to what to look for as I read the article. I will them make a data table to and evidence table to document the findings.

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I am in the process of reviewing my evidence table. I am comparing the evidence table to the tables that I created during this class.  More than likely I will be make some changes to the evidence table; however, it may be more beneficial to add a data table. By adding a data table I will be able to have a quick reference so that I may compare data that I collect from my DNP project to that of all the studies I incorporate into the project.

# data[].attributes.body
Atzema, C. L., Austin, P. C., Tu, J. V., & Schull, M. J. (2011). Effect of time to electrocardiogram on time from electrocardiogram to fibrinolysis in acute myocardial infarction patients.  Canadian Association of Emergency Physicians, 13(2), 79-89.  doi: 10.2310/8000.2011.110261

Critical Appraisal Skills Programme (2018). CASP (Cohort Study) Checklist. [online] Available at: https://casp-uk.net/wp-content/uploads/2018/03/CASP-Cohort-Study-Checklist-Download.pdf: Date Accessed July 31, 2018.

Engineering statistics handbook (n.d.).  Define sampling plan.  Retrieved from:  https://www.itl.nist.gov/div898/handbook/ppc/section3/ppc33.htm

Heavey, E. (2018). Statistics for nursing (3rd ed.). Burlington, MA: Jones & Bartlett Learning. ISBN-13:9781284142013.

Ladner, S. (2008).  Sampling in qualitative and quantitative research: A practical how-to.  Retrieved from:  https://www.slideshare.net/sladner/sampling-methods-in-qualitative-and-quantitative-research-presentation

Maier, G., & Martins, E. A. (2016). Health care for patient with acute coronary syndrome according to quality indicators. Rev Bras Enferm, 69(4), 757-764. doi: 10.1590/0034-7167.2016690420i

Neill, J. (2003, February,21).  Analysis of professional literature class 5:  Quantitative research design: sampling and measurement.  Retrieved from:  http://www.wilderdom.com/OEcourses/PROFLIT/Class5QuantitativeResearchDesignSamplingMeasurement.htm

Pearlman, M. K., Tanabe, P., Mycyk, M. B., Zull, D. N., & Stone, D. B. (2008). Evaluating disparities in door-to-EKG time for patient with noncardiac chest pain. Journal of Emergency Nursing, 34(5), 414-18.  doi: 10.1016/j.jen.2007.07.002

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